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AI for Sales Forecasting & Sales 

Process Execution 

How artificial intelligence provides more  

direction for forecasting and more wins

April 2017

In this issue








How to Create & Manage Your Forecast … 
So You Can Trust It 






6 Tenets of  an Accurate Sales Forecast 




Research From Gartner: Market Guide for SaaS-Based 
Predictive Analytics Applications for B2B   



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“You missed the number? Hey, no problem, you’ll 
get it next quarter!” said no CEO or board member 

Credibility in hitting the number is absolutely the 
most important thing for a sales leader. And 
these leaders that hit their number regularly, 
don’t have it easy. It’s usually a grind with a mad 
scramble at the end of  the quarter to hit plus or 
minus 5%. It doesn’t have to be such a painful 

Artificial intelligence for sales forecasting builds 
trust. If  you can trust what you see in the 
pipeline and that your pipeline is filling at the 
appropriate rate with qualified leads and 
opportunities that fit your ideal customer profile, 
you can have confidence that you’ll hit your 

Before I tell you what’s needed to have a forecast you 
can trust – let’s look at how we create and manage a 
forecast today.

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How “Not” to Forecast

If  you’re like most execs in sales or sales ops, you’re 
armed only with a couple of  reports from your 
CRM and a spreadsheet. You list and manage the 
opportunities by sales rep and by stage – then you 
spend a lot of  time in conversations with your sales 
team. Eventually you contact them daily for status 
updates. Conversations are great with the sales 
team – especially on strategy to close deals, but it’s 
such a waste of  valuable time to do daily status calls 
between manager and sales reps on the same deals. 
You still end up with minimal visibility and knowledge 
about deals. And this leaves very little time for selling, 
coaching and strategizing – among other priorities.

When you print out the pipeline for the team – it 
shows 3x to 4x the number. Questions you should ask 
yourself. Can I rely on that 3x to 4x pipeline number? 
How do I find out how good that pipeline really is? Can 
I rely on my weighted average number? Is there an 
easier way to get visibility into deals? What do I have to 
do to get this forecast to where I can trust it?

Additional side effects that your current sales 
forecasting process causes:

Because the current sales forecasting process is such 
a time suck, you hardly have any time left for:

1. Coaching and developing the sales team 
2. Work on maximizing conversions throughout each stage 
3. Ramping new sales reps 
4. Understanding the effects of  my lead-gen effort

And by not spending enough time on these four things, 
closing business and quota attainment suffers.

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How to Create & Manage Your 

Forecast …So You Can Trust It

Sales Automation for the Sales Forecast

Imagine if  you had an expert coach that knew your sales 
process and prompted you on a logical next step on a 
deal that was still active but you were starting to neglect? 
Or if  all of  your deals magically went to the appropriate 
stage and milestone and gave you an accurate forecast. 
These are examples of  what automation can provide. 
Automation is valuable because it saves a ton of  time, 
and it organizes and applies discipline and rigor without 
the manual effort. With process, workflow, and pipeline 
hygiene automation, an entire sales team will have a 
lot more time for selling and more value-added activity. 
These efforts are normally manual, time consuming and 
error prone.

The Use of Artificial Intelligence

A.I. provides an order of  magnitude of  valuable 
information and insights for sales forecasting. By 
applying the disciplines of  sales automation for sales 
forecasting, data can be analyzed and insights derived. 
For example, insights can alert you to whether you have 
enough pipeline to hit the number this quarter or next 
quarter, and it can tell you what to do about it, such as 
how much to increase Average Selling Price, or increase 
lead gen efforts or conversion rates.

Do not use BI tools for sales analysis!

Leave the Business Intelligence tools for accounting, 
HR or other departments where the data is more 
manageable. Using BI against untamed data in the CRM 
is almost as bad as using the spreadsheet. BI tools in the 
sales department
 usually end up as great looking graphs 
displaying insufficient and inaccurate data regurgitated 
from the CRM. These tools lack the automation of  a 
sales forecasting application and the artificial intelligence 
to keep the pipeline with clean hygiene, realistic deals 
and accuracy.

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6 Tenets of  an Accurate 

Sales Forecast

So, how do you apply sales automation and AI to 
your sales forecast?

The following are 6 strategies that can be utilized 
through automation and A.I., that will enhance the 
sales forecast as well as provide additional benefits to 
increase win rates and quota attainment.

1. Prescribe rules for the sales process

Create rules for advancing and regressing leads and 
opportunities as well as when and why to close them 
as a loss. Keep everyone from management to the 
sales rep on the same page about process rules. 
Discipline around when to move a deal, why, and 
where it belongs will keep things well organized and 
consistent. Here is a great resource on how to build, 
tweak or overhaul your sales process - How to Build a 
Winning Sales Process Guidebook

2. Enforce the sales process

CSO Insights research shows a 23% increase in quota 
attainment occurs when a rigorous sales process is 
used. By understanding how a deal flows through the 
funnel you’ll have accurate information on where deals 
get stuck and conversion rates. T
his is important data-
driven coaching information. And it’s not enough to 
just have a process. Many do, but it’s just written down 
somewhere or a powerpoint print out is pinned up on a 
cubical wall and therefore loosely adhered to.

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There needs to be reminders to the sales reps on the 
next step that follows the appropriate sales process. 
These steps have a time limit if  done effectively. 
Automation serves sales reps in this way – increasing 
the likelihood of  a sale.

3. Enforce pipeline hygiene

The bane of  every sales manager and sales rep is 
the current state of  pipeline analytics. There are too 
many deals that are not accurate, not up to date, in 
the wrong stage or just don’t belong in the pipeline. 
You don’t have to spend the hours scrutinizing deals 
for hygiene or increase the coverage of  pipeline to 
compensate – you just need to have the disciplines of  
hygiene to be aware of  better quality deals with higher 
probability of  wins. You can solve this problem by 
reading further in this document.

4. Make it easy to update the CRM

Sales reps need to provide their point of  view on deals.
These status updates need to be in the CRM as soon 
as possible after any meeting or communication with 
prospects and customers.

5. Capture “the right” signals for Artificial 

Artificial intelligence can provide great insights, 
but false signals could distort prescribed insights 
for decisions. Quality signals such as emails from 
prospects and meetings from the sales teams’ 
calendars are helpful to determine opportunity quality.

6. Analyze how deals flow through the funnel 
by rep and by time

Now that you have 1 through 5 of  the strategies in 
order, managers and sales reps have sufficient and 
accurate data amplifying their ability to get real 
insights. This guidebook explains how to 


performance and customize coaching

 to get the entire 

team exceeding quota.


By applying automation and AI in this way, you’ll be 
able to trust your sales forecast, while developing and 
making your sales team better. No longer will you 
wonder if  the 3x or 4x pipeline coverage are really 
comprised of  good deals. Conversations that you are 
having with the sales reps will make their way into 
the CRM for better analysis and decisions. Ramp 
time of  sales reps will be quicker, providing faster 
times to quota attainment. And you’ll have time along 
with data-driven analytics to better coach the team. 
And thank goodness – you can finally get rid of  that 

To learn how our customers are benefitting from these 
strategies - 

click here


To see how sales automation and artificial intelligence 
can be applied to give you a consistent sales process 
and accurate forecast with TopOPPS - 

request a demo


Source: TopOPPS

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Research From Gartner

Market Guide for SaaS-Based 

Predictive Analytics Applications 

for B2B

SaaS-based predictive analytics applications are 
helping B2B salespeople and marketers improve win 
rates, deal velocity and size. IT application leaders 
must understand the dynamics of  this emerging and 
rapidly growing category and identify providers that 
can support these business units.

Key Findings


The market for SaaS-based predictive analytics 
applications is still small and nascent (Gartner 
estimates its worth at $100 million to $150 million 
by the end of  2016), but it offers a compelling ROI 
potential that should lead to rapid growth within 
the next two years.


Many vendors, particularly those that target 
marketing (rather than sales) users, now offer broad 
solution suites that address many different use 
cases — from segmentation to account selection, 
demand generation and upsell/cross-sell.


Applications are typically purchased using short-
term subscription contracts (two years or less), 
and vendor churn at the end of  contracts remains 
high due to unrealized expectations and/or the 
ease of  switching.


While differentiation exists based on focus, go-to-
market strategy, integrations, functionality and/or 
data sources, many vendors use similar messaging 
and positioning, which causes confusion for buyers.

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IT application leaders who support marketing and sales:


Help your marketing team by investigating 
and adopting SaaS-based predictive analytics 
applications to improve segmentation, account 
selection, demand generation and lead scoring 
to increase conversion rates and contributions to 
pipeline and revenue.


Help your sales team by investigating and using 
SaaS-based predictive analytics applications to 
improve forecasting, pipeline management and 
upselling/cross-selling to increase win rates, deal 
velocity and average sales price.


Do not make purchase decisions solely on model 
performance during proofs of  concept; also 
consider factors such as data sources, integration 
options, industry expertise, customer references 
and overall customer experience.

Market Definition

This document was revised on 4 October 2016. The 
document you are viewing is the corrected version. For 
more information, see the 


 page on

This market encompasses an emerging category 
of  effectiveness and productivity applications for 
B2B sales and marketing professionals. Software 

as a service (SaaS)-based applications are used at 
different points of  the sales funnel for both prospects 
and existing customers. While traditional CRM lead 
management and sales force automation (SFA) offer 
some functionality to help marketers and salespeople 
make more effective decisions and are starting to 
incorporate artificial intelligence (AI) and machine 
learning, most of  the analytics they incorporate 
are based on predefined rules and diagnostic and 
descriptive analytics.

The solutions in this market leverage a range of  
predictive and, in some cases, prescriptive analytics 
techniques and models to enable better decision 
making based on a combination of  internal and 
external data at both account and contact levels. They 
also use machine-learning techniques to improve 
accuracy over time as more data is added to the model.

The market includes two discrete types of  solutions. 
One set of  applications is typically used by marketers 
(or, in some cases, sales development reps [SDRs]) 
and covers a set of  use cases higher up in the sales 
funnel). The use cases are shown in Figure 1. The 
models include both fit (propensity to buy) and intent 
(likelihood that a company is actively looking). A 
combination of  first-party internal data from CRM lead 
management and SFA systems, along with third-party 
external data from the web, proprietary and public 
databases, is used to build the models. 

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Source: Gartner (September 2016)

Figure 1. Predictive B2B Marketing Use Cases

The other set of  applications is typically used by those in sales roles, including sales leadership, frontline sales 
managers, sales reps, SDRs and sales operations leaders. They include a set of  use cases for the middle and latter 
part of  the funnel, as well as with existing customers. These use cases are shown in Figure 2. While some external 
third-party data may feed the model, the models for predictive sales applications rely more heavily on first-party data 
from SFA systems, as well as emails and calendar appointments. In some cases (particularly for upsell/cross-sell), 
data from ERP systems and data warehouses are included in the model. 

Many vendors that sell solutions to cover the marketing use cases also provide models for upsell/cross-sell 
identification. And while demand generation models can provide accounts and contacts for use in CRM lead 
management systems, SDRs and sales reps can use those same models for prospecting. In addition, Gartner 
expects to see vendors moving down or up the funnel to cover additional use cases, so both types of  solutions 
are being included in a single Market Guide. Buying processes for these types of  solutions are often led by IT 

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Source: Gartner (September 2016)

Figure 2. Predictive Sales Use Cases

or analytics teams, particularly for larger enterprises or those outside of  high-tech companies. But since the 
solutions are all SaaS-based, many of  the buying processes for emerging and high-growth technology providers 
are led by demand generation/marketing operations and/or sales operations, with IT playing an advisory or 
supporting role.

Predictive applications designed to improve renewal rates and optimize pricing are treated as discrete markets by 
Gartner and are no longer included in this guide. While SFA vendors have some opportunity scoring capabilities, 
that is not the primary purpose of  the applications, so they are not included. Vendors that offer predictive B2B 
marketing or sales functionality as part of  a service rather as a stand-alone product (including ServiceSource 
and Revana) or through a data or advertising platform (including Madison Logic) or those that only very recently 
added predictive capabilities to their platforms (including Avention) are not included. Finally, solutions that use 
rule-based approaches (but not data science techniques) are not included.

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Market Direction

The market has grown and matured since the 
introduction of  the last Market Guide in early 2015, 
but it still exhibits the characteristic of  a fast-growing, 
yet immature, early-stage market. Adoption has 
largely occurred from larger or high-growth technology 
providers in the U.S. However, both predictive B2B 
marketing analytics and predictive sales analytics 
have been included in Gartner Hype Cycles for the 
last two years. They both have “high” benefit ratings, 
“emerging” maturity ratings and market penetration 
rates of  5% to 20% of  their target audiences. 
Predictive B2B marketing analytics is at the Peak of  
Inflated Expectations, while predictive sales analytics 
is positioned at the beginning of  the Trough of  
Disillusionment. Given the characterization of  the 
market, adopters may see significant benefit but also 
experience the trial and error and potential need to 
switch vendors that come with this type of  market.

Gartner estimates that the market will see $100 million 
to $150 million in vendor revenue by the end of 2016. 
Despite the market’s comparatively small size, Gartner 
believes the aggressive positioning in the Hype Cycles 
is justified because of the high growth potential (both 
outside the U.S. and in other B2B industries) and the 
compelling ROI that clients have achieved using these 
types of solutions. The market has exhibited signs of  
maturity (especially in the high-tech industry in the U.S.), 
and vendors in the space have improved their solutions 
(as well as the customer experience), allowing buyers 
to move past the pilot or proof-of-concept stage and roll 
out the applications more broadly. As a result, buzz has 
increased, and Gartner has seen a noticeable uptick in 
client inquiries, including from clients in financial services, 
life sciences, business services and other industries.

Several trends have led to increased demand for 
solutions. First, account-based marketing (ABM) has 
emerged as a key investment area for many B2B 
companies, and predictive analytics can improve both 
account selection and demand generation elements. 
Many predictive B2B marketing analytics providers were 
quick to position their solutions as key enablers of  ABM.

Next, forecasting and pipeline management have become 
more challenging for many B2B sales leaders as buyers 
exert ever more control over their buying processes. 
Many leading SFA tools are the system of  record for 
forecasting and thus provide only basic forecasting 
capabilities. Sales operations teams often have to 
spend hours managing forecasts in Excel or business 
intelligence (BI) tools on a regular basis. Predictive sales 
analytics applications not only provide huge productivity 
boosts by automating this largely manual task, but 
also provide greater accuracy and visibility around the 
expected outcome of  individual deals, as well as the 
likelihood of  meeting forecast targets.

Finally, both lead management and SaaS SFA 
applications have reached mainstream adoption, with 
the former at 20% to 50% adoption and the latter at 
more than 50% adoption in the 2016 Hype Cycle for 
CRM sales. Many adopters of  predictive analytics have 
used one or both of  those solutions for three to five (or 
more) and have the capacity and desire to take on new 
projects. With predictive solutions starting at $25,000 
per year, many of  the more sophisticated B2B 
marketing and sales operations leaders have started 
to look at predictive analytics as a potential answer to 
some of  their more vexing problems.

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All the vendors in this guide are privately held and 
are either venture-backed or “bootstrapped” with 
capital from their own founders. There has been some 
consolidation since the last Market Guide, with smaller 
vendors exiting the market. For example, Fliptop was 
acquired by LinkedIn, and SalesPredict was purchased 
by eBay, in both cases for their machine-learning 
capabilities, and bought C9. However, 
new entrants have more than made up for those exits.

Gartner does not expect to see a new market entrant 
that solely addresses the current use cases through 
2017 (at least in North America) but does expect 
more traditional vendors, such as Oracle or Adobe, 
to enter through acquisition of  one of  the existing 
predictive B2B marketing vendors. Salesforce and 
Microsoft have acquired AI companies and may also 
choose to make a purchase or investment to round out 
their existing capabilities (although both have equity 
investments in at least one vendor in this guide). Most 
predictive vendors were able to raise money before 
the venture capital downturn in late 2015 and early 
2016, and some are also aiming for cash-flow break-
even in the next six to 12 months. Nevertheless, the 
market (especially inside the U.S.) is crowded, and one 
or more vendors in this guide may find it difficult to 
survive as an independent company.

With the need to become profitable and the burden 
of  acquiring local data, most North America-based 
vendors have focused close to home and shied away 
from international expansion, at least in terms of  
targeting companies outside North America or hiring 
salespeople in other regions. (They do support 
international sales and marketing teams from North 
America-based customers.) A few vendors included in 
this guide are based in the U.K. or France, and they 

expect to target Germany and other Western European 
countries in the next year. No vendors report targeting 
Australia, Asia or Latin America in any meaningful way. 
For the marketing use cases, data can be an issue in 
certain countries, particularly those with double-byte 
character sets. (Fuzzy logic matching is the most 
problematic in Asia.)

Since the last Market Guide, the predictive models 
have moved from being predominantly “black box,” 
where the signals that drive the models are hidden, 
to being more open and transparent, which Gartner 
believes is a positive step. However, IT and sales and 
marketing leaders need to be careful to figure out 
what signals they want to expose to sales reps within 
the account, opportunity and lead objects in the SFA 
system. It is crucial to find the right mix between 
providing enough information to build trust and 
making the data more actionable versus providing too 
much and confusing the rep or SDR.

Differentiation remains an issue for most vendors 
discussed here. On the predictive B2B marketing side, 
most vendors utilize similar data science techniques, 
create models that can self-tune, support the same 
use cases, source similar third-party data (including 
intent data from Bombora and others) and offer 
rapid turnaround (or self-service) model creation. The 
model creation time used to be a differentiator, but 
that has largely been erased. The lack of  apparent 
differentiation and the typically short contracts (12 to 
18 months is common) have made it easy for vendors 
to poach customers away at the end of  their contracts.

On the predictive sales side, similar differentiation 
issues exist, especially because external data is 
less important. There are some more clear points 

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of  differentiation across both marketing and sales, 
and they are called out as part of  the sample vendor 
write-ups. While model accuracy in a proof  of  concept 
is an important factor, Gartner recommends that IT 
application leaders who support sales or marketing 
(and anyone in the buying process) also consider a 
wide range of  factors, including focus, integrations, 
customer references and product vision, when making 
recommendations or decisions.

Market Analysis

More than 20 vendors offer SaaS-based predictive 
analytics applications specifically for use by B2B 
sales and marketing professionals. While outbound 
SDRs (and some sales reps) get engaged at the top 
of  the sales funnel in a prospecting capacity (through 
emails or phone calls), most predictive use cases at 
the top of  the funnel are the domain of  marketing 
professionals, including those in demand generation, 
marketing operations and product marketing.

Here is more information about the different types of  
predictive B2B marketing Use Cases:


Total addressable market (TAM) identification — 
B2B companies often want to understand how big 
of  an opportunity exists before entering a market 
or making staffing and investment decisions. While 
a TAM number may exist, not all companies in 
the market are easily addressable. The predictive 
models can identify the size of  the market (both in 
revenue and number of  accounts) for which their 
solutions would address and the total roll-up of  
all companies in a market with a fit score above a 
certain level. Sales operations leaders can also use 
this data for territory planning purposes.


Vendors: EverString, GrowthIntel, Infer, Mintigo, 
MRP, Radius


Segmentation — Predictive models can be used 
to create segments of  accounts based on signals 
(fit or intent) rather than traditional firmographics. 
These groups of  accounts can be the basis for 
campaigns in lead management systems or 
segment-based ABM programs. As predictive 
signals change, the segments change with them.


Vendors: 6sense, BrightTarget, Datanyze, 
EverString, GrowthIntel, Infer, Lattice Engines, 
Leadspace, Mintigo, MRP, Radius, SalesChoice


Account selection — One of  the fastest-growing 
predictive use cases is to identify the best 
accounts to select for an ABM program. Marketers 
use predictive models to highlight anywhere from 
a few dozen to more than a thousand accounts 
and tier them based on propensity to buy (fit, 
intent or both). The accounts are then exported for 
campaign orchestration to lead management, web 
personalization and advertising platforms.


Vendors: 6sense, BrightTarget, Datanyze, 
EverString, Infer,, Lattice Engines, 
Leadspace, MarianaIQ, Mintigo, MRP, Radius, 


Demand generation — While some B2B 
organizations (particularly those with subscription-
based offerings, free trials and freemium solutions) 
are blessed with more inbound leads than they 
can effectively manage, most are not. Marketers, 
SDRs and sales reps (both generally and as part of  
account-based programs) are constantly looking 

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to expand the people to whom they prospect and 
have turned to predictive-driven solutions instead 
of  traditional lists. Vendors offer predictive models 
to identify companies based on fit and intent and 
then deliver contacts that can be exported to lead 
management or SFA systems.


Vendors: 6sense, Datanyze, EverString, 
GrowthIntel, IKO System, Infer, Lattice Engines, 
Leadspace, MarianaIQ, Mintigo, MRP, Radius


Lead scoring — Predictive lead scoring was the 
initial marketing use case and far away the most 
mature one. Traditional lead scoring is based on 
two dimensions (demographic/firmographic and 
engagement), while predictive lead scoring makes 
use of  more (and more relevant) signals that are 
correlated with propensity to buy to go along with 
engagement and/or intent. These models have 
generally proven to be far more accurate than 
traditional lead scoring at predicting the likelihood 
of  a lead converting into an opportunity and 


Vendors: 6sense, BRIDGEi2i, BrightTarget, 
Datanyze, DxContinuum, EverString, Infer,, Lattice Engines, Leadspace, 
Mintigo, MRP, Radius

Fewer vendors offer solutions to address sales 
rather than marketing use cases, although many of  
the marketing vendors do provide upsell/cross-sell 
models, which share similarities with other solutions 
they offer. But while the marketing models rely heavily 
on external data, the sales models are more reliant on 
internal data.


Forecasting — As the system of  record, traditional 
SFA tools often lack the forecasting and pipeline 
management capabilities required by sales 
operations leaders, while the data going into 
the forecasts (typically entered by the sales rep) 
often lacks the rigor and accuracy that sales 
organizations require. Predictive forecasting 
models solve both problems by automating 
the forecasting and pipeline management 
processes and using data science models to score 
opportunities and roll them up at various levels. 
Sales leaders and managers can see the forecast 
revenue at product, team or geographic levels, 
while reps can gain better insight for their own 
opportunities and quota attainment.


Vendors: Aviso, BRIDGEi2i, BrightTarget, Clari, 
DxContinuum,, SalesChoice, 


Opportunity scoring — Predictive forecasting has 
replaced the need for stand-alone opportunity 
scoring for many B2B companies, but there are 
still situations where opportunity scoring can be 
helpful for both sales reps and their managers. 
Understanding the true likelihood of  close (and the 
close date) instead of  going off  what the rep has 
entered alone can help dictate focus and attention. 
Vendors are also moving toward giving prescriptive 
guidance and coaching (also true with the 
forecasting solutions) to help reps and managers 
understand how to improve the likelihood of  
closing a deal.


Vendors: BRIDGEi2i, Clari, DxContinuum, Infer,, SalesChoice, TopOPPS

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Upsell/cross-sell — Many larger and more 
established companies add far more revenue from 
growing existing accounts versus signing new 
ones, so predictive upsell and cross-sell models 
have been around for longer than most other use 
cases. The applications primarily rely on internal 
data, but not all of  it is in CRM systems. Models 
that incorporate transactional data from order 
management systems and data warehouses 
are usually more accurate. Some vendors have 
extraction, transformation and loading (ETL) tools 
or various data layers to get at that data, while 
others need the customer to provide extracts. The 
models provide not only the accounts to target, but 
also the solutions to offer. Some B2B companies 
build their own systems at first, but the care and 
feeding of  the system often drive a switch to a 
third-party solution.


Vendors: 6sense, BRIDGEi2i, BrightTarget, 
DxContinuum, Entytle, EverString, Infer, Lattice 
Engines, Mintigo, Radius, SalesChoice

Representative Vendors

The vendors listed in this Market Guide do not imply an 
exhaustive list. This section is intended to provide more 
understanding of the market and its offerings.

Note: The “high-tech industry” includes technology 
vendors (hardware and software), service providers and 
communications service providers (CSPs).


Use Cases: Segmentation, account selection, demand 
generation, lead scoring, upsell/cross-sell

San Francisco-based 6sense was created in 2013 and 
has raised $46 million in venture funding. It targets 
marketers and supply sales leaders with outbound 
prospecting tools that identify when buyers are in-
market, helping them answer the answer of  “timing.” 
It also offers lead scoring and upsell/cross-sell 
predictions. While its customers certainly leverage 
6sense’s account and contact/lead fit-based models, 
6sense has invested more heavily in time-based, intent 
modeling techniques than any other vendor in this 
guide. 6sense’s patented methodology predicts when 
prospects are in an active buying cycle and where 
the prospect is in his or her buying journey. It has 
built a private data network that includes publishers, 
search engines, blogs, community forums and many 
other sources (and augments data from other intent 
providers). 6sense also utilizes IP to company matching 
and cookie syncing and incorporates time-based and 
relativity-based predictions into its models to gauge 
intent before someone fills out a form or “raises a 
hand.” 6sense leverages its publisher relationships 
to allow marketers to reach their buyers through 
their ABM efforts. The time-based intent modeling 
capabilities feature prominently into the company’s 
positioning and messaging and customer testimonials.

6sense recently introduced a lower entry price to appeal 
to midmarket companies, but it has historically targeted 
large enterprises in high-tech and other verticals. The 
lower entry pricing and shorter-term contracts now 

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make it easier for customers to test and buy 6sense’s 
solutions. 6sense is often a good fit for companies that 
are in highly competitive markets, where understanding 
buyer readiness (timing) is critical. 6sense was named a 
Gartner Cool Vendor in 2015.

Industries Represented: Financial services, high tech, 
manufacturing, medical devices, professional services

Supported Integrations: Bombora, Forbes (and other 
large publishers), Integrate, Madison Logic, Marketo, 
Oracle (BlueKai and Eloqua), Salesforce

Notable Customers: BlueJeans Network, Dell, 
GE, NetSuite

Pricing: Starts at $50,000 per year for midmarket 
companies (higher for larger companies) and 
increases based on the number of  products that are 
being modeled


Use Cases: Forecasting

Menlo Park, California-based Aviso has been in business 
since 2012 but released its first predictive forecasting 
solution in early 2015. It has 65 employees and has 
raised $23 million to support its efforts. The company 
creates an integrated forecasting view across all 
revenue sources that is completely consistent from 
the global level down to the individual BU, region or 
rep. This is built on top of  a forecasting engine that 
utilizes predictive and prescriptive analytics models 
to determine the likelihood of  a deal closing, the 

date it will close and how much it will be worth. Aviso 
provides capabilities to help sales operations and 
sales leadership get early warnings and easily see the 
discrepancies between traditional and Aviso forecasts 
and dynamically highlights the recent changes that have 
impacted its models. Aviso’s architecture has the ability 
to incorporate multiple data sources, including CRM, 
email and calendar data, in its models. One of  the 
capabilities it believes to be unique is modeling around 
billings, revenue and pipeline, instead of  just bookings 
data (to better predict the actual size of  the deal). 
Aviso also provides automated alerts when its models 
indicate changes, such as a deal being likely to slip.

Aviso focuses on companies with more than 50 sales 
reps. While it supports other SFA systems, a large 
fraction of  the company’s clients use Salesforce. 
Aviso has been in this market for less than two years 
but already has more than 40 customers. It is a good 
fit for midmarket and enterprise customers across 
several industries. Aviso is able to create forecast 
models against multiple SFA systems simultaneously, 
which can allow customers not to have to rush to 
integrate SFA systems after making an acquisition.

Industries Represented: High tech, manufacturing, 
media, professional services

Supported Integrations: Gmail, Microsoft Dynamics, 
Microsoft Exchange, NetSuite, Oracle, Salesforce, SAP

Notable Customers: Hewlett Packard Enterprise, 
Marketo, Nutanix, Splunk

Pricing: Aviso has several editions of  the product, but 
it starts at $900 per user per year, with a $20,000-per-
year minimum spend.

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Use Cases: Lead scoring, opportunity scoring, 
forecasting, upsell/cross-sell

Bangalore, India-based BRIDGEi2i is the only 
vendor in this guide headquartered outside North 
America, the U.K. or France. The company started 
as a predictive analytics consulting firm in 2011 
and rolled out its first product in 2014. Most of  its 
customers are in the U.S., but it also has some in 
Asia (in India, in particular). BRIDGEi2i offers stand-
alone opportunity scoring, as well as a predictive 
forecasting solution. The solutions leverage Monte 
Carlo simulations, and both managers and reps can 
do scenario modeling and what-if  planning in their 
native application or through a third-party 
visualization tool. Reps can also benchmark their 
expected performance against those of  peers. For 
sales leaders and sales operations leaders, BRIDGEi2i 
can offer recommendations to help them act on the 
data. The company also offers a stand-alone upsell/
cross-sell model and a lead scoring solution, based on 
fit and intent (although it leverages fewer external data 
sources than most other solutions in this guide).

BRIDGEi2i targets large companies in a variety of  B2B 
industries (it has 10 customers in the Fortune 100 
alone) with solutions that are more custom-designed 
rather than off  the shelf. It most commonly replaces 
homegrown solutions. Many of  its customers have 
internal data scientists; BRIDGEi2i’s professional 
services team works closely with them around 
potential solutions. Although some of  its solutions 
are immature and lacking in functionality when 

compared with competitors’, the professional services 
capabilities can often fill in the gaps. BRIDGEi2i’s 
flexibility and common data model make it a fit for 
very large companies that want an alternative to more 
packaged options from other vendors.

Industries Represented: Consumer packaged goods 
(CPG), financial services, high tech (manufacturing), 
insurance, retail

Supported Integrations: Marketo, Salesforce

Notable Customers: Not indicated.

Pricing: Starts at $50,000 per year and increases 
based on customizations, number of  products, scope, 
models and sales team size


Use Cases: Segmentation, account selection, lead 
scoring, forecasting, upsell/cross-sell

West Midlands, U.K.-based BrightTarget started as an 
innovation department within a BI/data consultancy 
in 2012 and was established as a separate business 
in 2014. It was known as Kairos until late last year, 
when it sold off  the consulting firm. While BrightTarget 
sells to high-tech companies in the U.K., it focuses 
on industries that others have paid less attention to, 
including building services and media. The dataset it 
builds also reflects these priorities as the BrightTarget 
Business Index includes data on small building 
companies. BrightTarget also differentiates by taking 
a customer lifetime value (CLTV)-driven approach to 

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its modeling, showing those values even when scoring 
leads. As the market has matured, BrightTarget 
has done the same thing by significantly reducing 
the time to develop models and rolling out a new 
lightweight forecasting tool. BrightTarget also offers 
marketing attribution capabilities and will roll out TAM 
identification and demand generation solutions in the 
coming months.

BrightTarget remains focused on the U.K. market and 
the current industries for which it has had success. 
Upsell/cross-sell and account scoring (from purchased 
lists) remain its entry points. The CLTV-related 
capabilities address a common pain point for Gartner 
clients as they look to identify the best accounts to 
target for expansion. As BrightTarget rolls out more 
top-of-the-funnel solutions, it will have an opportunity 
to scale its business but will face greater competition, 
as well.

Industries Represented: Building services, high tech, 

Supported Integrations: Adobe Marketing Cloud, 
Force24, Marketo, Oracle (Eloqua), Salesforce (Pardot 
and Sales Cloud)

Notable Customers: BSS Industrial, Company Check, 
Euromoney Institutional Investors, Speedy Hire

Pricing: Starts at $32,000 per year and increases 
based on the number of  models created and the 
amount of  data being used


Use Cases: Forecasting, opportunity scoring

Sunnyvale, California-based Clari has been in existence 
since 2013 (with a product in 2014) but traces its 
predictive analytics legacy back to 2005. The company’s 
founders (and much of  its staff) came from machine 
pioneer Clearwell, which was sold to Symantec in 
2011. Clari has raised $46 million to date and delivers 
predictive models for pipeline inspection, deal and 
forecast management — what Clari customers call the 
“opportunity to close” process. It has a wide range of  
prepackaged integrations (although it supports only 
Salesforce among SFA vendors). Clari was the first to 
bring email and calendar data into forecast and deal 
models. The company can track forecast and deal detail 
changes in real time without exporting, and it features a 
“graph” that prioritizes sales reps’ tasks across key deals 
to drive better productivity and a “grid” that helps both 
reps and managers with real-time updated deal progress. 
Clari recently announced its AI-driven messaging 
platform that proactively prescribes actions to sales 
teams (called “nudges”) to drive behavior and actions 
that increase deal velocity and close probabilities.

With a higher starting price than some of  its 
competitors, Clari focuses on pre-IPO, private-equity-
backed and public companies, mainly in technology, 
media and professional services. Clari is a good fit for 
upper-midmarket companies and enterprises that are 
looking at applying predictive analytics to drive better 
pipeline management and more accurate forecasting 
and use Salesforce for SFA.

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Industries Represented: High tech, media, 
professional services

Supported Integrations: Box, Dropbox, Evernote, 
Gmail, LinkedIn, Microsoft Exchange, Salesforce

Notable Customers: Hewlett Packard Enterprise, Intel, 
Juniper Networks, Palo Alto Networks

Pricing: Clari declined to provide pricing details for 
this guide. Please contact the vendor directly for 
pricing details.


Use Cases: Segmentation, account selection, demand 
generation, lead scoring

Four-year-old San Mateo, California-based Datanyze 
has been in the predictive space for only a year, but 
it has built up a strong following in other areas (more 
than 30,000 users) by tracking technology and mobile 
installs and alerting SDRs when an account has 
installed a particular product. (Datanyze also provides 
a free browser plug-in.) Datanyze evolved from a 
technographic information provider into a full-fledged 
data platform (with 45 million domains and contact 
information) and then added predictive models for 
both marketers and SDRs to better take advantage 
of  this data. They can also use the data platform 
to enrich account, lead or contact information. The 
company currently leverages only fit models, but it 
has its own IP tracking capabilities so that anonymous 
website traffic can be easily added to them. Intent is 
also derived by looking at whether an account recently 
added a competing or complementary product.

The predictive capabilities are packaged as an add-on 
to the data platform, but the total solution price (data 
and models) is still lower than most, if  not all, other 
predictive solutions. The company focuses exclusively 
on selling to technology companies (especially 
SaaS providers). While Datanyze gets a foothold in a 
company through the technology tracking capabilities 
being used by SDRs, the data platform solutions are 
also purchased by marketers to help with demand 
generation. The predictive add-on is increasingly being 
purchased, especially for top-of-the funnel processes.

Datanyze has raised only $2 million in venture funding 
(in a 2014 seed round). The company is a fit for 
emerging SaaS companies looking for cost-effective 
predictive demand generation solutions. Datanyze was 
named a Gartner Cool Vendor in 2016.

Industries Represented: High tech (primarily SaaS)

Supported Integrations: HubSpot, Marketo, Salesforce

Notable Customers: HubSpot, Marketo, Namely, 
New Relic

Pricing: The data management solution with predictive 
add-on starts at $20,000 per year and increases as a 
result of  customizations and additional model creation.


Use Cases: Lead scoring, opportunity scoring, 
forecasting, upsell/cross-sell

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Fremont, California-based DxContinuum is one of  
the newer players in the space (with a product since 
2014 and funded in 2015) and takes one of  the 
more unique approaches to its targeting and product 
portfolio strategies. The company mainly targets sales 
operations and sales leadership with an upsell/cross-
sell solution then adds on an opportunity scoring 
solution that rolls up into a predictive forecast. It 
also offers a lead scoring solution. DxContinuum 
typically deals with more complex sales processes, 
and it offers a data preparation layer to transform 
data to more easily be used in a model. The models 
it creates are highly sensitive to changes in data and 
how often that data changes. DxContinuum has several 
unique capabilities, including the creation of  a family 
of  models with different regression techniques for a 
single use case (selecting the one that produces the 
best fit) and even the ability to allow a customer to 
run the solution on-premises. DxContinuum is one of  
a few vendors in this guide with a Salesforce security 
certification and will be introducing a Salesforce Wave 
Analytics capability later in 2016.

As one of  the smaller and least capitalized vendors in 
the market (it closed $4 million in a Series A round in 
3Q15), the company is mindful about staying focused 
on a “sweet spot.” While DxContinuum will pursue 
midmarket deals opportunistically (companies with 
at least 50 reps), it has typically sold to much larger 
technology companies, with a complex product mix 
that uses Salesforce. The security, data transformation 
and deployment options are far more important for 
larger companies. DxContinuum is a good fit on the 
sales operations side for larger high-tech companies 
(including those with a large indirect channel), 
especially around upsell/cross-sell use cases.

Industries Represented: High tech

Supported Integrations: Marketo, Oracle (Eloqua and 
Sales Cloud), Salesforce

Notable Customers: Adobe, Akamai, Cisco, VMware

Pricing: $30,000 per year for 25 users; additional 
costs for additional users


Use Cases: Upsell/cross-sell

Mountain View, CA-based Entytle is one of  the newer 
players in the space (with a product since 2014 and a 
product shipped in 2015). It focuses on an area of  the 
market that has largely been ignored by other vendors. 
Entytle sells aftermarket “entitlement automation” 
solutions to industrial manufacturers. The solutions 
utilize predictive models to identify low-wallet-share 
customers and then recommend upsell and cross-sell 
opportunities for spare parts, consumable items and 
service contracts. Data from a range of  applications 
(including ERP, contact center, service and support) 
feeds the model, either through real-time integration 
or lightweight extracts. Since many machines and 
other industrial products through an indirect channel 
don’t currently “phone home,” Entytle’s solutions 
help manufacturers infer usage and behavior largely 
based on the “traces” they leave on different systems 
and through network data of  similar devices and 
manufacturers. Data is presented for individual 
solutions and bundles at both an account and 
opportunity level. Entytle has raised $8 million in seed 

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funding and will release a campaign planner tool and a 
contract upsell capability later in 2016.

Entytle typically targets discrete manufacturers 
with more than $200 million in revenue that want 
to upsell spare parts or service contracts, replace 
consumables, manage their field workforce, or gain 
better visibility into the state of  their installed base. 
While other vendors offer predictive solutions for 
upselling and may compete with Entytle in deals for 
high-tech manufacturers, Entytle is often unopposed 
when selling to industrial equipment manufacturers. 
The aftermarket entitlement-specific use case comes 
up only in a handful of  predictive-analytics-related 
client inquiries today, but with more than $1 trillion 
spent annually on industrial aftermarket purchases, 
Gartner expects to see increased interest in solving 
this problem moving forward.

Industries Represented: High tech (hardware and 
manufacturing), industrial manufacturing

Supported Integrations: Marketo, Microsoft Dynamics, 
Salesforce (Pardot, Sales Cloud and Service Cloud), 
SAP, ServiceMax

Notable Customers: Hayward Gordon, Johnson 
Controls, Philips Healthcare, Teledyne

Pricing: Starts at $100,000 per year (plus a one-time 
set up fee of  $50,000) and increases based on the 
amount of  pipeline increased from  
Entytle-based recommendations


Use Cases: TAM identification, segmentation, account 
selection, demand generation, lead scoring, upsell/

San Mateo, California-based EverString started in 
2014 and quickly secured more than 100 customers 
and almost $79 million in venture capital funding. It 
supports the full gamut of  predictive B2B marketing 
use cases (including ones not in the guide, such as 
ad targeting) and some sales-related use cases, as 
well. EverString claims to take a different approach 
with its modeling than other vendors, preferring 
to look at a wider range of  signals to identify the 
company’s “DNA” rather than traditional fit and intent 
models. Its approach to creating data revolves around 
extracting machine-learning insights, in addition to 
crawling and ingesting data from both proprietary and 
commercial sources. The company also has separate 
algorithms for segmentation and scoring. EverString 
targets marketers, sales development managers and 
sales leaders and provides self-service capabilities 
for individual marketers, such as expansion audience 
building and segmentation on the fly. It helps sales 
reps and SDRs identify accounts and contacts both 
inside and outside their CRM system that are similar 
to the ones they just closed. EverString was one of  
the first predictive analytics vendors to promote its 
solutions as a more data-driven way to do prospecting 
and account selection for ABM.

EverString has used its venture capital investment to 
aggressively fund its product development and go-to-
market efforts. This has allowed the company to go 

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after a wide range of  industries and simultaneously 
serve the midmarket and enterprise segments. 
EverString has a low entry price that is attractive to 
smaller companies, but it can scale its offerings (and 
the associated price) to meet the needs of  much larger 
companies. Given its broad portfolio capabilities, 
EverString can be a fit for many North American 
companies looking at predictive B2B marketing or 
sales solutions. EverString was named a Gartner Cool 
Vendor in 2016.

Industries Represented: Financial services, 
healthcare, high tech, professional services

Supported Integrations: Marketo, Microsoft Dynamics, 
Oracle (Eloqua), Salesforce

Notable Customers: Apttus, Comcast Business, 
IBM, Salesforce

Pricing: Starts at $14,000 per year for the entry-level 
EverString Audience Platform and increases with 
database volume and additional use cases. Contacts 
for demand generation are priced separately.


Use Cases: TAM identification, segmentation, demand 
generation, upsell/cross-sell

London-based GrowthIntel was founded in 2011 
and has built its business selling top-of-the-funnel 
predictive solutions to U.K.-based companies that 
target small businesses. GrowthIntel’s solutions are 
mostly used by chief  marketing officers (CMOs) 

and demand generation leaders to build segments 
for outbound campaigns to net-new prospects, but 
it also offers models for prioritizing inbound leads 
and targeting existing customers. Unlike most other 
vendors in the guide, GrowthIntel collects primary 
data instead of  relying on third-party data. This 
encompasses more than 4 million small businesses 
in the U.K. and is augmented with credit reporting 
data from third parties and internal data to build the 
models (it currently has only direct integrations with 
Salesforce but can export to CRM lead management 
tools). Although each client’s data is always kept 
confidential, GrowthIntel also makes use of  a network 
effect across the interactions that its customers have 
with small businesses, which provides an additional 
level of  prediction beyond fit.

With only a few other vendors targeting the U.K. 
market and none of  them really focusing on the 
same type of  use cases/industries (segmentation 
and demand generation for companies targeting 
small businesses), GrowthIntel has had the market 
largely to itself. In client inquiries with Gartner, this 
company ends up being mentioned in conjunction 
with traditional data vendors rather than with other 
predictive marketing vendors, but GrowthIntel is a 
fit for U.K.-based companies that are targeting small 
businesses. The company plans to expand to France 
and Germany in the near future, which will broaden 
its market opportunity, assuming it can collect the 
primary data it needs, but it may also see greater 
competition in its home market from vendors in France 
and the U.S.

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Industries Represented: Financial services, 
high tech, logistics

Supported Integrations: Salesforce

Notable Customers: BT, Pure360, PwC, Zurich Reinsurance

Pricing: Starts at £50,000 per year. Pricing increases are 
based on the number of opportunities that were created.

IKO System

Use Case: Demand generation

Paris-based IKO System has been in the predictive 
demand generation market since 2012. It targets 
Western European B2B companies (mainly in France, 
but also in the U.K., the Netherlands and Germany) 
that are looking to generate more leads from net-new 
customers. Despite raising only €3 million in venture 
funding, it has acquired more than 200 customers over 
the last four years. IKO System scores accounts with 
an approach toward understanding an account’s “DNA” 
and scores the contacts it provides, as well. It will be 
adding a lead scoring capability for inbound leads later 
in 2016, but the company has also developed an inside 
sales solution to augment its portfolio.

Most other vendors in the market have ignored France 
and the rest of  Western Europe (outside the U.K.), 
and IKO System has capitalized on that void to sell 
predictive demand generation solutions. Given the 
breadth and quality of  the Europe-centric data it can 
utilize, IKO System is a fit for both local companies 
and regional arms of  U.S. companies that are looking 

for better demand generation options, especially in 
France. The inside sales solution may prove to be a 
useful addition to the IKO System portfolio because 
it is the only vendor in Europe that can currently 
provide the combination of  predictive scoring, net-new 
contacts, prescriptive guidance around channels and 
interactions to sales reps and SDRs, and automation 
to make that process easier. IKO System was named a 
Gartner Cool Vendor in 2015.

Industries Represented: Financial services, high tech, 
insurance, professional services

Supported Integrations: Gmail, HubSpot, Marketo, 
Outlook, Salesforce

Notable Customers: Infor, Talend, TIBCO 
Software, Tidemark

Pricing: Starts at €12,000 per year and increases 
based on volume. The inside sales solution is priced 


Use Cases: TAM identification, segmentation, 
account selection, demand generation, lead scoring, 
opportunity scoring, upsell/cross-sell

Mountain View, California-based Infer launched in 
2010 and has signed up more than 140 customers, 
the vast majority of  them being high-growth SaaS 
companies. The company has raised $35 million in 
venture funding to support its efforts. Infer provides 
separate fit and behavior models (with intent part of  it). 

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It also layers on a profile management capability (with 
access to thousands of  data points) for segmentation 
and account selection, and the profiles can be pushed 
into Salesforce and Marketo, enabling marketers to go 
beyond simple smart list creation in the latter solution. 
Infer has been actively promoting the value of  its 
solution for ABM efforts and has formed partnerships 
with Terminus and AdRoll on the activation side 
(integration is done via Salesforce). The company also 
supports unique cases, such as scoring any external list 
before marketers have to purchase them. Infer is one of  
only a few predictive B2B marketing vendors to provide 
models for opportunity scoring.

With a large volume of  SaaS companies as customers, 
Infer is a fit for midmarket and upper-midmarket SaaS 
companies, especially for account selection and lead 
scoring projects. However, newer entrants to the space 
have aggressively gone after Infer customers when 
their initial agreements are set to expire, causing the 
company to focus on defending its installed base. As 
Infer has expanded its solution set, the company has 
the opportunity to expand within its own customer 
base, but Gartner still expects to see Infer branch into 
other markets in 2017 and beyond. Infer was named a 
Gartner Cool Vendor in 2015.

Industries Represented: High tech (primarily SaaS)

Supported Integrations: Google Analytics, HubSpot 
(fit score only), Marketo, Oracle (Eloqua), Salesforce 
(Pardot and Sales Cloud)

Notable Customers: HubSpot, New Relic, 
Tableau, Zendesk

Pricing: Starts at $30,000 per year and increases 
based on the number of  models. Net-new contacts (for 
demand generation solution) are priced separately.

Use Cases: Account selection, lead scoring, 
opportunity scoring, forecasting

Provo, Utah-based is a unique player 
in the predictive B2B marketing and sales application 
market. Long known as a pioneer in the inside sales 
solution market with a platform that includes a dialer, 
email templates and gamification capabilities, the 
company has also had some innovative predictive 
analytics capabilities with its Neuralytics engine and 
NeuralView product. Using network data of  more than 
150 million customer profiles (based on more than 
100 billion interactions across its more than 3,000 
customers), the company’s NeuralSort capability can 
help SDRs predict who to contact, when to contact 
them, what to say and what channel to use. It can also 
score accounts, leads and opportunities, providing 
a NeuralScore based on propensity to close. In early 
2015, acquired C9 (a Gartner Cool 
Vendor in 2015) and added its predictive forecasting 
capabilities (now branded as HD Forecast) to the 
portfolio. The combined platform provides both 
predictive analysis and prescriptive recommendations, 
with the latter being among the most extensive of  
any vendor. has raised nearly $200 
million in venture funding, including investments from 
Microsoft and Salesforce. has made a shift from largely 
targeting small or midsize businesses to moving 
upmarket, with more than half  of  new sales coming 

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from enterprises. It is one of  the major players in 
the inside sales solution market, and NeuralView is 
an integral component (and differentiator) of  that 
platform. All the predictive capabilities are included in 
the highest pricing tier, which means that NeuralView 
users can add predictive account and lead scoring 
for free. does not provide leads or 
contacts for demand generation, so it rarely competes 
with other predictive B2B marketing vendors, but its 
predictive marketing solutions are a fit for a range of  
companies, especially those already using InsideSales.
com’s inside sales solution. The HD Forecast solution 
is a fit for enterprises running Salesforce or Microsoft 
that are looking to improve forecast accuracy and 
pipeline visibility.

Industries Represented: Business services, financial 
services, high tech, insurance, professional services

Supported Integrations: Microsoft Dynamics, Salesforce

Notable Customers: ADP, GE, Google, 
Thomson Reuters

Pricing: NeuralView is part of  the Accelerate edition 
subscription, which is $3,540 per user per year. HD 
Forecast Professional Edition costs $840 per user 
per year for opportunity scoring, while HD Forecast 
Enterprise Edition costs $1,080 per user per year and 
includes predictive forecasting.

Lattice Engines

Use Cases: Segmentation, account selection, demand 
generation, lead scoring, upsell/cross-sell

San Mateo, California-based Lattice Engines began 
offering predictive solutions in 2006 productized its 
initial offering (an upsell/cross-sell solution targeted at 
sales) in 2011. Lattice has more than 150 customers 
across both marketing and sales use cases (many of  
them are larger companies), has raised $75 million 
in venture capital, and by Gartner’s estimation, is the 
largest (by revenue) pure-play vendor in this guide. 
While Lattice’s more deliberate approach won deals 
with larger and more security-minded companies (both 
in high-tech and other verticals), it was not suitable 
for smaller companies. But in 2015 and early 2016, 
the company rearchitected its solutions to allow easier 
data integration and real-time performance; created 
a new user interface (with all models visible from a 
single portal); beefed up integrations with Salesforce, 
Oracle (Eloqua) and Marketo (to aid with ABM and 
other campaigns); expanded its data platform to 
include international, contact and intent data; and 
added self-service modeling capabilities. It also added 
account selection and demand generation solutions 
(which includes data diagnostics, such as cleansing 
and enrichment tools).

Despite the rapid growth of  several other vendors, and 
an internal focus in 2015 on rearchitecting its solutions, 
Lattice remains the most visible “face” of  the market. 
With its focus on security, level of  integrations and ETL 
tools, the company is a fit for enterprise clients (both 
in high-tech and other industries) and/or companies 
planning to deploy in multiple regions. Gartner clients 
report that the company’s go-to-market approach is 
unique in the way it addresses complex problems and 
help customers operationalize the insights from the 
models. Lattice is one of  the few vendors that can 

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recommend key plays at both the lead and account 
level across the entire funnel. Lattice was named a 
Gartner Cool Vendor in 2013.

Industries Represented: Business services, 
distribution, financial services, high tech, industrial 

Supported Integrations: Marketo, Microsoft Dynamics, 
Oracle (Eloqua), Salesforce (Pardot and Sales Cloud)

Notable Customers: Amazon, Dell, PayPal, 
SunTrust Bank

Pricing: Starts at $50,000 per year and includes 
unlimited models. The price increases based on the 
number of  contacts and sales users.


Use Cases: Segmentation, account selection, demand 
generation, lead scoring

San Francisco-based Leadspace was started in 2010 
to provide data for demand generation professionals. 
It built up a strong customer base around the quality 
of  its data. In 2013, it added statistical and machine-
learning models to help clients identify the accounts 
and contacts most likely to buy, both in terms of  
net-new companies and existing leads. Today, the vast 
majority of  Leadspace’s more than 120 customers 
are using predictive models as opposed to simply 
accessing data. However, data remains at the heart 
of  the Leadspace offering, and the company uses its 
Virtual Data Management Platform as a differentiator. 

The platform can easily bring in almost any kind of  
structured and unstructured data to help clients better 
understand the accounts and individual contacts. 
Leadspace’s approach is to take data, enrich it and 
then blend it with semantic knowledge to get around 
data accuracy issues that marketers often encounter. 
The company believes it not only makes campaigns 
and outreach more effective, but also allows for better 
segmentation at the persona level.

For most of  its existence, because of  its reputation 
for comprehensive and accurate data, Leadspace has 
competed against traditional data providers instead 
of  other predictive analytics vendors. (It would often 
expand its footprint once it sold data.) But over the last 
year and after the company’s last venture capital round 
(bringing it to a total of  $35 million raised), Leadspace 
has become more aggressive in its go-to-market 
approaches. The company tends to be a stronger fit 
for high-tech and professional services companies that 
have large house databases but lack confidence in the 
accuracy and the quality of  their data. Leadspace was 
named a Gartner Cool Vendor in 2016.

Industries Represented: High tech, professional services

Supported Integrations: HubSpot, Marketo, Oracle 
(BlueKai and Eloqua), Salesforce (Pardot and 
Sales Cloud)

Notable Customers: Autodesk, Microsoft, 
Oracle, RingCentral

Pricing: Based on a combination of  platform 

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functionality and data volume. Starts at $26,000 per 
year, which includes enrichment and predictive scoring 
for up to 50,000 records. Net-new predictive discovery 
of  accounts and contacts is available at additional cost.


Use Cases: Account selection, demand generation

Palo Alto, California-based MarianaIQ is the newest 
vendor in this guide. It was founded in 2014 but didn’t 
release a product or get a seed round ($2 million) until 
2016. The company also focuses on a unique segment 
on the market. Its primary capability is to help 
marketers reach target personas at named accounts 
through multiple channels, starting with Twitter and 
Facebook. While some of  its customers only use 
Mariana’s machine-learning capabilities to match 
social media profiles, others use the applications for 
account selection (based on fit and intent) for ABM 
programs. If  you know the names of  the people you 
want to target, MarianaIQ can identify Twitter handles 
and Facebook profiles based on first name, last name 
and email collected from a Salesforce database, but 
it can also match using fuzzy logic. Clients can also 
simply go with the profiles MarianaIQ recommends 
based on persona, segment or industry.

Compared with most other vendors in this guide, 
MarianaIQ covers a very narrow niche. But with the 
demand for ABM programs and the importance of  
Facebook and Twitter as advertising channels, there is 
potential for high demand for what it offers. Gartner 
has seen the most interest for this type of  solution 
from high-tech companies running ABM programs that 

have a more complex sales process and many buyers 
to target. MarianaIQ has a deep integration (both 
from an orchestration and reporting perspective) with 
Marketo and integrates with Salesforce (both Pardot 
and Sales Cloud) and HubSpot. It doesn’t currently 
integrate with Oracle Eloqua, making it a better fit 
for Salesforce customers running other CRM lead 
management systems. The company has developed 
some additional capabilities to help with lead 
nurturing and predict the right call to action, which 
may increase its appeal.

Industries Represented: High tech (primarily SaaS)

Supported Integrations: HubSpot, Marketo, Salesforce 
(Pardot and Sales Cloud), Twitter

Notable Customers: Finsync, MemSQL, 
WhiteHat Security

Pricing: Starts at $30,000 per year (without data) 
and $70,000 per year (with data). There are additional 
charges for persona creation and data volumes above 
10,000 contacts.


Use Cases: TAM identification, segmentation, account 
selection, demand generation, lead scoring, 

Founded in 2009, San Mateo, California-based 
Mintigo started out providing data for B2B demand 
generation professionals and added predictive models 
in 2012 based on propensity to buy. By 2013, it had 

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expanded its solutions to offer predictive lead scoring, 
opportunity scoring, account selection and upsell/
cross-sell solutions. Mintigo’s initial focus has allowed 
it to be much less reliant on external data providers 
than most other vendors, and Mintigo does its own 
intent validation, as well. The company can create 
custom attributes for clients, something it believes 
provides a competitive advantage. It includes real-time 
data enrichment to all lead, contact or account records 
in both CRM lead management and SFA systems, 
where the records are scored against a predictive 
model. It allays security concerns by not storing 
contacts in its system. For upsell/cross-sell models, 
Mintigo can score multiproduct solutions and bundles. 
Mintigo recently added prescriptive sales coaching 
capabilities at the lead and account levels and is in 
beta with a predictive campaign tool that automatically 
personalizes the message and content by persona.

Mintigo focuses on the upper end of  the midmarket and 
on enterprise accounts in a range of  B2B industries. It 
has raised $34 million in venture capital funding and 
was one of  the fastest-growing vendors in the market 
in 2015. Mintigo has fewer customers than some other 
predictive B2B marketing vendors that it competes with, 
but it has been very successful in getting a foothold 
within large enterprises and expanding solutions down 
the funnel and to support upsell/cross-sell use cases 
for large customers. Mintigo’s integrations, security 
capabilities and tools make it a good fit for companies 
considering more sophisticated ABM programs or when 
they have more ambitious or comprehensive strategies 
for predictive analytics.

Industries Represented: Financial services, 
high tech, media

Supported Integrations: Adobe Campaign, Integrate, 
Madison Logic, Marketo, Microsoft Dynamics, Oracle 
(Eloqua and Sales Cloud), Salesforce

Notable Customers: Getty, Oracle, Red Hat, SolarWinds

Pricing: Starts at $60,000 per year, with an additional 
charge for contacts for demand generation. The sales 
coaching solution is priced separately.


Use Cases: TAM identification, segmentation, account 
selection, demand generation, lead scoring

MRP is a wholly owned subsidiary of  Northern 
Ireland-based First Derivatives. MRP had partnered 
with Framingham, Massachusetts-based predictive 
analytics software provider Prelytix to power its 
Delta Marketing Cloud managed services for ABM. 
MRP acquired Prelytix in early 2015 and renamed 
the solution Delta Prelytix. MRP has more than 350 
customers across three continents leveraging its 
predictive analytics software, with many (but not all) 
using it to support the use cases that are included 
in this guide. MRP’s models are run off  the powerful 
Kx database (another First Derivatives portfolio 
company). The models are largely intent-based rather 
than fit-based. The intent signals help marketers and 
SDRs not only understand propensity to buy, but also 
better understand where prospects are in their buying 
journey to tailor the content and outreach accordingly.

MRP has one of  the largest customer bases and a 
greater geographic coverage than other providers 

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included in this guide. But because MRP also provides 
full-funnel managed services, many buyers don’t see 
it as a direct competitor to most other predictive B2B 
marketing vendors. MRP is a fit for companies that 
are looking to power ABM efforts into markets that are 
well-established or more transactional in nature and 
where intent is the primary criteria for sellers.

Industries Represented: Financial services, high tech

Supported Integrations: Marketo, Microsoft Dynamics, 
Oracle (Eloqua), Salesforce (Pardot and Sales Cloud)

Notable Customers: Cisco, CSC, Hewlett Packard 
Enterprise, NetSuite

Pricing: Starts at $60,000 per year and scales 
based on the countries covered, number of  solution 
topics and the level of  customization to the scoring 
algorithm, which can be modified and adapted to 
specific client needs.


Use Cases: TAM identification, segmentation, account 
selection, demand generation, lead scoring, upsell/

San Francisco-based Radius was founded in 2009 
and launched its first predictive B2B marketing 
application in 2014. It has raised more than $128 
million in venture funding, more than all but one 
vendor in this guide. Initially, the company was heavily 
focused around helping companies across a range 
of  industries better identify and target the small 

businesses that had the highest propensity to quickly 
purchase its solution. But over the last year, Radius 
has added capabilities to address a wider range of  
use cases (especially down-funnel) and to address the 
needs of  companies selling to enterprises. The Radius 
Business Graph of  20 million businesses includes 
account and contact data gathered from internal and 
external sources and is enhanced by the network 
data across interactions of  all its customers. It more 
recently expanded to include attributes that are 
more important for companies targeting enterprises. 
Radius has strong integrations with Salesforce and 
Marketo, as well as with Facebook, which allows users 
to activate custom audience campaigns to the target 
account lists and segments that Radius recommends. 
The company also promotes rapid self-service model 
creation to allow marketers to quickly size and engage 
with new segments.

Despite having more than 100 customers (with several 
in the Fortune 50) and being really well-funded, 
Radius has been more under the radar than some 
other vendors in the guide. It has been able to sell 
to a broader range of  clients (half  its customers are 
outside high tech), and it would often go unopposed 
with companies that sold to small businesses. But 
the company’s visibility has increased over the last 
year, especially around ABM-related use cases, such 
as account selection, demand generation and upsell/
cross-sell. Radius doesn’t charge customers until 
solutions are fully deployed, which removes the risk for 
smaller customers. Its security model and broad suite 
of  offerings make it a fit for larger customers, as well. 
Radius was a named a Gartner Cool Vendor in 2016.

Industries Represented: Financial services, high tech, 
insurance, media, office supplies, travel

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Supported Integrations: Facebook, Marketo, Oracle 
(Eloqua), Salesforce

Notable Customers: American Express, Expedia, First 
Data, Sam’s Club

Pricing: Starts at $42,000 per year and increases based 
on the size of  the database and additional use cases


Use Cases: Segmentation, account selection, 
opportunity scoring, forecasting, upsell/cross-sell

Toronto-based SalesChoice was founded in 2012 
but released its first predictive analytics solutions in 
2015. The company has an early U.S. patent filing for 
predictive and prescriptive sales analytics, leveraging 
diverse signals inside and outside the CRM system. 
In 2016, it added two new product offerings to its 
suite: Prescriptive Analytics and Intent to Purchase 
(Propensity Signals). The company is small, with 
fewer than 50 employees, and has taken no outside 
investment to date. While it recently introduced a 
propensity-to-buy module to select accounts to target 
for upsell/cross-sell purposes and it can do stand-
alone opportunity scoring, its biggest focus is around 
predictive forecasting for companies in the U.S. and 
Canada. It uses many diverse machine-learning 
methods to identify the likelihood of  a win, prescribe 
actions and increase the odds of  winning. SalesChoice 
also leverages AI and predictive constructs to 

prescriptively guide sales reps around discounting 
and prioritization. Each company gets its own unique 
predictive model versus a subset of  predictive 
attributes, enriching the pattern intelligence.

More than any other company in this guide, 
SalesChoice leverages partnerships with Salesforce-
based system integrators, including Accenture and 
RelationEdge. It will sell direct, but it can also be part 
of  larger initiatives led by those partners to improve 
sales effectiveness within high-tech companies. 
SalesChoice isn’t as visible as some other predictive 
forecasting vendors (in part because the lack of  
outside investment limits its marketing budget), but it 
is a fit for high-tech, professional services and media 
companies running Salesforce or large enterprises 
that are looking to take on bigger initiatives to improve 
sales effectiveness or ABM (particularly for smaller 
volumes of  accounts).

Industries Represented: High tech, media, 
professional services

Supported Integrations: Salesforce (Sales Cloud, 
Salesforce1 Mobile App and Wave)

Notable Customers: Accenture, Digiday, RelationEdge

Pricing: Predictive and prescriptive bundle is $750 
per seat per year. Intent to Purchase (upsell/cross-
sell) is $360 per user per year plus data fees. Volume 
discounts are available.

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Use Cases: Opportunity scoring, forecasting

St. Louis-based TopOPPS has been in the predictive 
sales analytics market since 2014. It started off  
selling a stand-alone opportunity scoring solution and 
later added a forecasting solution. The company’s 
core philosophy is to help sales leaders (executives, 
operations leaders and managers) enforce better 
behavior on the part of  sales reps by offering more 
accurate information about whether a deal is good 
or not. TopOPPS also enables admins to build their 
own models. Unlike most others in this guide, the 
opportunity score is driven not by likelihood of  close, 
but whether it’s “healthy.” That health assessment is 
done through SFA data but can also use email and 
calendar information (through Salesforce, although 
not a direct integration). TopOPPS then leverages 
prescriptive analytics to suggest ways to improve 
the health of  that opportunity through embedded 
coaching tips and alerts. Information about what 
has changed (and why that has impacted the score) 
is easily accessible. The company can also provide 
metrics to sales leaders around whether a rep is 
winning deals at the appropriate rate, selling at the 
right price and holding on to deals for too long.

Despite having more than 50 customers, TopOPPS 
isn’t as visible or well-known as some other predictive 
forecasting and opportunity scoring vendors, While it 
certainly sells its ability to improve pipeline visibility 

and forecast accuracy for emerging companies, 
TopOPPS’ focus around impacting sales behavior by 
more accurately representing the health of  a given 
opportunity and suggesting ways to improve it allows 
the company to target a different type of  customer. 
TopOPPS’ approach is a good fit for large and more 
established companies that struggle to change sales 
rep behavior because of  the long tenure of  reps and/
or a culture that is more resistant to change.

Industries Represented: Distribution, high tech

Supported Integrations: Microsoft Dynamics, 
NetSuite, Salesforce

Notable Customers: Buckner Companies, Eventbrite, 
Interactive Intelligence, TriZetto

Pricing: Starts at $14,000 per year (30 users) and 
increases based on additional users


Founded in 1998, Austin, Texas-based Zilliant has 
long been known for its predictive price optimization 
solution, but it also offers SalesMax, a predictive 
analytics application to help sales reps easily 
identify what they should be selling to existing 
customers. The company is focused squarely on 
selling to industrial manufacturers and distributors 
that have repeat purchase relationships with their 
customers, differentiating Zilliant from many other 
vendors providing upsell/cross-sell solutions, and 
the recommendations from its models are sent to 

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sales reps via email, but are also available via mobile 
devices, the web and in Salesforce. Zilliant’s roadmap 
includes a number of  capabilities slated for later 
in 2016 to expand its account-centric approach, 
with capabilities to model account-level revenue/
profit potential (based on wallet share) and to better 
understand historical performance.

In addition to targeting different industries than 
most vendors in this guide, Zilliant also addresses 
a different set of  sales processes. There are more 
than 25 companies using SalesMax, and the average 
number of  accounts per rep is between 50 and 200. 
To cover such large territories, many of  those reps 
have mandates to visit or call on up to 10 customers 
per day. They often look at SalesMax before a call or 
in the parking lot before meetings to learn what they 
should propose when they get inside. Zilliant claims 
that 83% of  the opportunities that are presented by 
SalesMax get acted on (pursued) by the reps. Reps can 
also use SalesMax to identify what customers to visit 
for upcoming sales trips.

Industries Represented: Distribution, industrial 
manufacturing, high tech

Proven Integrations: Salesforce, SAP

Notable Customers: Dayton Superior, FleetPride, IMI 
Precision Engineering, Lincoln Electric

Pricing: Starts at $2,500 per sales per year, with a 
minimum of  25 reps

Market Recommendations

IT application leaders should talk with their sales and 
marketing stakeholders to understand if  there is an 
opportunity to use these solutions to overcome more 
complex buying processes or to increase the overall 
effectiveness of  their teams. Despite the buzz, the 
market is new enough that many of  your stakeholders 
may not even know there are predictive analytics 
solutions available that they can operate to solve their 
problems. And if  you work for a larger company, there 
may be one of  more of  these solutions already being 
used at a department or business unit level (especially 
on the marketing side) without broader awareness at 
either the IT or business level.

As you evaluate vendors, accuracy should be only 
one of  many considerations. While noticeably 
worse accuracy would be a potential reason for 
disqualification of  a vendor during a bake-off, it’s 
unlikely that one vendor will greatly outperform 
another one. Instead, IT application leaders should:


Work with your stakeholders to understand the 
dynamics of  how your customers buy and how 
your salespeople sell. Because in many cases, the 
vendors can’t easily differentiate around modeling 
techniques and third-party data sources, they 
will often try to differentiate around go-to-market 
approaches, especially who they target.


Look for the vendors that have customers similar to 
you (and talk to their references), and pay attention 
to what is really important to you from an IT 
standpoint and your stakeholders from a business 
standpoint. Some vendors are better-equipped to 
deal with security concerns, others have modeling 

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approaches that favor a particular market type or 
culture, others may have better integrations with 
key systems, and yet others may provide a better 
customer experience after the sale.


Clearly understand your own internal capabilities, 
especially for more complex use cases. Even 
though these predictive solutions abstract the 
data science from the users, your stakeholders 
may be overwhelmed if  they do not receive 
adequate guidance from the vendors. Unless you 
have internal data science expertise or external 
consultants at the ready, ensure that the vendor 
you select has capabilities and programs to assist 
with that needed guidance.


Consider whether ease of  use and sales rep 
acceptance are important relative to other criteria. 
Some solutions have easier-to-use interfaces that 
are designed for rapid adoption by sales reps, 
but that may come at the cost of  functionality or 
signal exposure.


Ensure that the first-party data you provide to 
vendors is high quality, especially for marketing 
use cases; while the data doesn’t have to be 
perfect and the vendors can cleanse and enrich 
to some extent, low-quality data can significantly 
impact model performance.

Gartner also recommends, before committing to 
automated updates, that you ask vendors to be as 
transparent as possible about how their predictive 
algorithms work. The algorithms to these solutions 
are far more transparent than in the past (a sign 
of  maturity), but you will still not have configurable 
control over the calculation rules or be able to control 
the results. The vendors will generally adjust the 
algorithms as needed. In fact, some firms provide a 
dedicated data analyst to your account — someone 
who regularly reviews your results and makes 
adjustments as needed. This service is useful for 
mitigating the uncertainty that comes from using 
a black-box service, but this does not completely 
mitigate the risk involved here. Understanding how 
the algorithms operate is the best guard against 
unexpected outcomes.


Gartner conducted interviews with all the vendors 
listed in this guide in June and July 2016. This 
research was also supported by interviews from 
July 2014 through June 2016 with clients and other 
enterprises that have implemented those solutions in 
the U.S. and Western Europe.

Source: Gartner Research Note: G00303128, Todd Berkowitz, 
7 September 2016

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